I am trying to run a mixed model logistic regression with participants nested within state with a certain amount of covariates. Here is what my model looks like: m1 <- lmer(Overweight ~ age + factor(A_RACE_G) + Prevalance + HH_Income_Dicot + Unemployment_Rate + Intensity_effect + (1 | state2) + GSD_EFFECT + FMA_EFFECT + BMI_EFFECT + DBS_EFFECT + NSM_EFFECT + NCF_EFFECT + ACF_EFFECT + NES_EFFECT + AMS_EFFECT + FTS_EFFECT + PRI_EFFECT + PUI_EFFECT + MLR_EFFECT + PES_EFFECT + PAS_EFFECT + PFA_EFFECT + HES_EFFECT + SRS_EFFECT + SWP_EFFECT + TAX_EFFECT + WBP_EFFECT, data = reduced, family = binomial (link = "logit"), REML = FALSE) My main concern is that the model does not run when I add in ", weights=A_FINALWT" which is the survey weighting variable. 1: In eval(expr, envir, enclos) : non-integer #successes in a binomial glm! 2: glm.fit: algorithm did not converge 3: glm.fit: fitted probabilities numerically 0 or 1 occurred That is the error I receive. The model will run if I use the continuous BMI as the dependent variable and run it without family = binomial. However, we want a logistic regression, not a linear one. What is it that is not working with weighting the model by my sampling weight? [[alternative HTML version deleted]]